Assessing academic writing: L1 English content professors’ accommodation to non-standard rhetorical organization in L2 student writing
Bibliographic record
Abstract
Abstract \nAssessing academic writing: L1 English content professors’ accommodation to non-standard rhetorical organization in L2 student writing \n \nMargaret Levey \n \nIt is estimated that second language (L2) speakers of English in the world now outnumber first language (L1) English speakers more than 3 to 1. This shift in balance necessitates a re-examination of the notion of Standard English as L2 speakers develop regional and functional variations of English. In academic writing, Standard English is based not just on discrete elements of the language, but also on culturally determined rhetorical organization, which L2 scholars are expected to master to succeed in academia. Research suggests that in English academic publishing, the insistence on this culturally-defined rhetorical organization results in the unintentional silencing of the voices of L2 scholars. Yet whether the same insistence exists for university class assignments has been under investigated. Studies on the differences in the rhetorical organization of student-written compositions in languages other than English have not considered reader response. Conversely, studies exploring reader response to L2 writing have focussed on sentence-level errors rather than on rhetorical organization. \nUsing think-aloud protocols to access the thought processes of L1 content professors as they assess L2 student writing presented in both standard and non-standard rhetorical organization, this study employs a framework of critical discourse analysis to investigate whether L1 professors at a large Canadian university with a significant international student body accommodate to non-standard rhetorical organization in L2 student writing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".